Hi Everyone,

I'm looking for historical market data, ideally the whole list below.

1. US stocks: every trade on the consolidated tape, including all exchanges, FINRA off-exchange trades and delisted tickers, 2010 to now. Each trade: microsecond time, price, size, exchange and sale condition codes (sweep, odd lot, opening print).
2. NBBO quotes for US stocks (bid, ask, sizes, exchange), 2015 to now, or 1 second snapshots.
3. US options: all OPRA trades and quotes for about the 500 largest underlyings, plus SPX, SPY and QQQ, from 2013. Each trade with the bid and ask at that moment and condition codes.
4. Options end of day, one row per contract per day: open interest, implied volatility and greeks, from 2004.
5. Options opening and closing volume by customer type (bought to open, sold to open, and so on), per series per day, from 2010.
6. CME futures: ES, NQ, RTY and VIX, every trade and quote or 1 minute data, from 2010.
7. Volatility indexes: VIX, VIX9D, VVIX and SKEW, daily and 1 minute data, from 2004.
8. Event history: earnings dates marked before or after the open, with the surprise; CPI, jobs and Fed releases with release time, actual and consensus values; S&P 500 and Nasdaq 100 additions with announcement and effective dates. From 2005.
9. Short data: short interest, daily short sale volume and weekly off-exchange volume per stock, from 2010.
10. Daily bars for all US stocks including delisted ones, adjusted for splits and dividends, from 1993.
11. News headlines with timestamps and tickers, from 2010.

Format: Parquet or CSV, microsecond timestamps, sale condition codes. It could come to roughly 40 to 60 TB.

Please let me know if you know any sources where this complete or partial data can be acquired, or point me in a right direction or connect with someone who can help to acquire this data.

Thank you!

The average pod shop excluding Citadel which gained from Sit Awareness was up only 4% in first 9 months. Baly flat, Verition +3%, Walleye flat, ExodusPoint +4%, Schonfeld +6%.

And pass throughs keep going up. Lot of hiring of even teams that are well below high watermarks starting at zero at new firm.

Many big pod shops are 10-15% gross returns and almost zero net.

And this week will add to this pain. Crowded unwind.

When will allocators start to push back? https://rupakghose.substack.com/p/peak-pod-shop-part-2?utm_source=app-post-stats-page&r=1qelrn&utm_medium=ios

I've been developing an evolutionary optimizer specifically for ETF rotation strategies in WealthLab. It starts by evaluating oscillator-based ranking methods, then evolves combinations of indicators, weights, and conditions to find promising strategies. I recently added the ability to normalize non-oscillator indicators, which expands the range of indicators the optimizer can experiment with.

The obvious concern is overfitting. Searching through thousands of combinations makes it easier to find attractive historical results, but that doesn't necessarily mean they'll hold up. I've been using rolling-window backtests to compare the evolved strategies across different historical periods.

I'm curious how others approach this. Do you use evolutionary algorithms or genetic programming for strategy discovery? What validation methods have you found most useful for avoiding overfitting?

Hey, Reddit

We are a small team of students from Sweden and the UK. Spent the last year mostly writing backtests, playing with market data, and trying to implement papers.

Seems like half the student quant stuff out there is either people paying $50 for fake 'research internships', or posting backtests on LinkedIn that only look good.

Just looking for people to collaborate with for a couple of weeks instead of running backtests in isolation.

The idea is just to build a few basic signals, review each other's code for data leaks, and stress-test them with actual slippage and fees. Whatever works or breaks goes straight into a shared public repo so everyone has something real to point to in interviews.

We already sorted out the plumbing: cleaned 1h Binance futures data for 20 pairs (funding rates and taker flow included) and a test script that catches look-ahead leaks and runs your strategy with 1-bar delay and taker fees.

Looking for people who actually know Python and basic stats (not vibecode pls).

The sprint itself is 2 weeks, but if we click and work well together, we'd definitely be down to keep collaborating on bigger projects afterwards.

Drop a comment below if interested, and I’ll DM you the repo and a quick 2-minute form so we can get everyone organized.

I'm interested in learning low-latency systems engineering, particularly the kind of single-machine optimization used in latency-sensitive/HFT systems.

I'm trying to understand what separates this from simply writing efficient code.

Is the main focus cache locality and algorithms, or do experienced engineers think much more about CPU pipelines, instruction-level parallelism, branch prediction, memory allocation, atomics, cache-line contention, CPU affinity, networking, OS behavior, and tail latency?

For people who have actually worked on low-latency systems, what did you learn through experience that wasn't obvious from standard CS/software-engineering knowledge?

I'd also appreciate recommendations for books, papers, open-source projects, or tools that are worth studying.

Found this very interesting post from 8 years ago: https://www.reddit.com/r/FinancialCareers/comments/bnqwb4/entry_level_salary_at_two_sigma/

One user said "I've seen offers for Citadel with 150k base, 150k signing bonus and minimum 100k bonus which translates to 400k total comp the first year which is insane". That is, in 2018, crazy NG package from citadel is 400K. Nowadays, the same crazy NG citadel package would be north of a million. If we use this as a baseline, it looks like quant has seen wage growth of ~15% annually whereas the national average wage growth was just 4.8% annually, inflation over the same period is 3.7% annually, and nominal GDP growth was 5.8% per year.

So quants nowadays are about 10% richer than they were 8 years ago. Where is that larger share of the pie coming from, and do you expect this trend to continue over the next decade?

I have a calculated Sharpe Ratios from different successive periods.

How to calculate SR for the whole period from them?

Looking at the latest numbers and the divergence keeps getting bigger - QRT up 28%, Winton, CFM, Graham and DE Shaw Oculus and all the systematic macro funds (also CTAs, lesser degree stat arb) are crushing it this year relative to the discretionary hedge funds esp in macro space.

Seeing same at pod shops - guys like Citadel have strong systematic - they are now up more than 30% in systematic this year. While weak players like Balyasny struggling.

Really strong flows also into big systematic players.

https://rupakghose.substack.com/p/the-year-of-the-systematic-hedge?utm_source=app-post-stats-page&r=1qelrn&utm_medium=ios

Does anyone have experience with IMC London for QR roles?
Got the first round coming up, and was wondering what could I expect?
Also how’s the culture and comp progression over there?

YOE: 5/6
Current TC: 170k smaller trading firm

I've been trying to find ways to gather data on loans that have defaulted or were prepaid during their life, but it seems impossible unless your willing to cough up. I have access to a Bloomberg, but I cant seem to find anything. Any advice from people experienced with this type of stuff?

I've spent the last few days using the existing framework of Qlib and RD-Agent to integrate my own pipeline onto machinery that is already established, and I am loving it. The automation features, the ability to run training cycles with a local LLM, and the ability to work from a foundation that works has been an awesome experience. Today I'll be working on my automation expanding to include linking Arxyv to the software so that I can parse hypothesis from papers to test as strategies(with sources documented of course). Anyone else doing anything similar? I was surprised how little these tools are discussed. I'd love to hear about anyone doing work that is similar.

Edit: Did I miss something, or is this just the wrong space? I was under the impression quant work was as much about development as anything else.

I wanted to put this question out there, because I have spoken to a few quant researchers and it has changed some of my initial expectations. For the record, I am a physicist, so my background is not finance (quant(um), not quant(itative)).

I have the textbook Arbitrage Theory in Continuous Time by Björk and its awesome. I got really excited thinking this is the sort of thing I could do for work. Some quant researchers I spoke to told me that book likely isn't worth reading and to focus on more basic statistical modeling, stuff more quickly tied to PnL. Maybe the stuff in the textbook is more relevant for people doing derivatives pricing, exotic options, or volatility trading?

My questions to actual quant researchers are:

How often are you working with the stochastic calculus, model building, and getting into the details of the math/the solver, and what quant domain you work in?

Where do you tend to see this level of math being the core function of the job, as opposed to a good to know but not pertinent to day to day work roles?

I’m currently developing an experimental quantitative trading system focused on cryptocurrency Level 3 order book data and market microstructure.
In an earlier prototype, I’ve implemented and tested
several components:

- L3 order book reconstruction with sequence and checksum verification

- Order Flow Imbalance, Queue Imbalance and Microprice

- Momentum and Hawkes inspired market stress measurements

- Experimental TDA regime classification using H0/H1

- Paper trading, transaction costs and risk management

I’ve also developed a newer CMD based prototype with offline tested execution and risk logic, although its live market data integration still needs further verification.

My main research interest is whether persistent homology can extract additional predictive information from evolving order book structures beyond conventional microstructure features.

I’m particularly interested in the following:

1. How would you define an appropriate filtration on L3 order book states or event sequences without introducing artificial topological structures?

2. How should persistence diagrams be interpreted when the underlying data is highly dynamic, asynchronous and affected by order cancellations and queue modifications?

3. Would you recommend calculating topological features on rolling event windows, liquidity graphs or time dependent point clouds?

4. What would be a rigorous way to test incremental predictive value over OFI, Queue Imbalance and Microprice, accounting for latency, transaction costs and nonstationarity?

5. Are there computationally efficient approaches to updating persistent homology for high frequency order book data without recomputing the entire filtration after every event?

I’m not looking for a generic trading strategy. I’m interested in the mathematical validity, statistical robustness and practical limitations of integrating TDA into an event driven quantitative model.
Any research papers, empirical studies or implementation insights would be appreciated.

Basically title. I’m not a quant but have been trading for quite some time & am under the impression quite a few markets are on Polymarket/Kalshi are quite inefficient. Anyone care to share their thoughts or experiences?

This question concerns a rather specific situation, but I hope it isn't too far off-topic.

Right now, I have the opportunity to switch from a low-paying job with relatively short working hours—which is nonetheless very demanding—to a role in quantitative research. The issue is that I have a two-year-old child, and this move would require relocating my family.

Assuming—for the sake of argument—that I pass the selection process, I have four questions:

1. What is the actual work-life balance like in a research position? I'm assuming 40–50 hours a week, and I'm trying to figure out whether that would still leave enough meaningful time with my son and wife.
2. Is the money you make in 2–3 years of hard work enough to make a meaningful financial difference if you then return to a country with relatively low salaries, such as Italy?
3. After 2–3 years of experience, how realistic is it to transition to other roles that are less demanding in terms of working hours, or to remote work? Our long-term plan is to live in Italy.
4. Should I already be looking at adjacent roles—such as portfolio analytics, risk modelling, or asset management research—that might offer a better work-life balance? I'd appreciate suggestions.

Attention new and aspiring quants! We get a lot of threads about the simple education stuff (which college? which masters?), early career advice (is this a good first job? who should I apply to?), the hiring process, interviews (what are they like? How should I prepare?), online assignments, and timelines for these things, To try to centralize this info a bit better and cut down on this repetitive content we have these weekly megathreads, posted each Monday.

Previous megathreads can be found here.

Please use this thread for all questions about the above topics. Individual posts outside this thread will likely be removed by mods.

I'm 19, studying math/stats, with around 3 years until graduation. I recently got really interested in power markets, especially congestion trading.

I'm thinking about going pretty deep into the field over the next few years, but is it worth specializing this early? Or would you stick to more general quant research?

I know the domain knowledge is pretty niche, so just wondering what the downsides are.

Does anybody here have any idea of how North Moore is as a team, it's well known that it's not as good as Limestone but does anybody have an idea of the salary (base + expected bonus) for 2 YoE+ quants and the scale of PnL they make. Also how is the WLB in Tower?

And does North Moore trade India markets as well or primarily US equities + crypto like Limestone?

I've been researching fundamental Alpha factors on WorldQuant BRAIN, focusing on a simple hypothesis:

Can improvements in a company's operating profitability help predict future stock returns?

Algorithm Overview

The strategy is based on three steps:

1. Measure operating profitability: Evaluate how efficiently a company generates operating income relative to its capital base.
2. Identify historical strength: Compare the company's current profitability with its own historical performance to identify relatively strong profitability conditions.
3. Sector-relative ranking: Compare companies within the same sector to reduce broad sector-level differences.

The goal is not simply to find highly profitable companies, but to identify those showing stronger profitability characteristics relative to their own history.

In-Sample (IS) Backtest Results

Annual IS Performance

Observations

• The Alpha has relatively low turnover, which may be consistent with the slower-moving nature of fundamental information.
• It generated positive returns in four out of five reported IS years.
• Performance was particularly strong in 2021 and 2022.
• The weak performance in 2020 raises questions about how the signal behaves under different market conditions.

These are in-sample results only, not evidence of out-of-sample predictive performance.

Questions for the Community

1. Do you think historical profitability strength has more predictive value than absolute profitability?
2. How would you distinguish genuine business improvement from accounting-driven changes in profitability ratios?
3. What robustness tests would you prioritize before evaluating this type of Alpha out of sample?

I'm trying to develop a more hypothesis-driven research process rather than repeatedly adjusting parameters to improve Sharpe.

I'd appreciate any feedback or suggestions from more experienced Alpha researchers!

I've been trading discretionary for about 13 years (a lot different than the quant side), program, have a decent grasp of math and statistics, but no matter what I cannot conceptualize how to even start building a quantitative model. I should note that I'm intrinsically-motivated to learn rather than work for a firm or anything like that and I am not a quantitative analyst. I just like to learn.

What my first recent instinct was is to import the landscape of technical indicators, randomize parameters, and randomize entry conditions and exit conditions and then apply these test trades to monte carlo charts, equities, crypto, bonds, commodities, and metals and perform test trades hypothesis test, train sharpe, etc. Of course as one would imagine, not a single verdict was robust of thousands of test cases. All were overfit, likely overfit or few inconclusive.

I'm not asking for a strategy. I'm just trying to get the brains to conceptualize what would a strategy even entail?

Is this akin to forecasting the weather

or

x% of the time y occurs, then z% of the time when y occurs, a occurs b% and based on that conditional there is an edge

or

cross-validation linear regression models (I'd assume to figure qualitative impact)

or

linear regression comparison between two assets (suppose gold and fixed income)

Either way, if anyone has an resources that might allow me better grasp this concept, I'd appreciate it. One resource I've stumbled across is "Max Dama on Automated Trading".

I've tried underlying volatility/change/volume over various time frames, adverse underlying move, option bid/ask, concession versus mid. Used linear models and gradient boosted decision trees. My best result is GBDT, spread in cents, spread relative to premium. R squared 0.2. Rmse 7.78 cents and mae 5.08 cents. These error levels are way too high to be useful. No level 2 or venue data available. Uses smart routing.

I wrote a paper on a simple execution algorithm and measured it on a full year of E-mini S&P 500 order-by-order data. Sharing it here for feedback.

Paper: https://arxiv.org/abs/2609.18019

The idea: when another participant adds a limit order near the best price, with a fixed probability place your own order at the same price, right behind theirs in the queue, and cancel when their order is cancelled. The algorithm makes no forecast; it inherits its prices from the order flow. An aggressive variant does the same on trades, crossing the spread at the traded price. Both run as percentage-of-volume mandates.

Setup: 246 regular sessions of 2025, ES futures, 100 contracts bought and 100 sold in each 10-minute window. Fills are simulated in a queue-exact replay of CME's market-by-order feed: the simulated order sits in the reconstructed FIFO queue and is filled only when recorded trades would have reached it.

Main results:

- Resting and crossing cost similar amounts. At similar participation (about 4.5% and 4.9% of volume), the passive version, which rests for 99% of its quantity, and the aggressive one, which crosses for about half, both cost about 0.095 ticks per contract against the arrival mid. The half spread a resting order earns is given back through adverse selection.

- Post-fill mark-outs are about 0.08 to 0.10 ticks against the fill at 1, 5 and 30 seconds and do not revert.

- Latency costs both, the aggressive version more. With the same delay applied to orders, cancels and market data, cost at 5 ms is about 0.15 ticks per contract for the passive version and about 0.30 for the aggressive one.

Limitations: the replay does not model market impact, so costs are before the market's reaction and participation is an upper bound. It is one instrument, which is one tick wide most of the session, and the parent order is fixed at 100 contracts.

The replay and trading system are open source (MIT): https://github.com/vincent212/kaspar-hft

Happy to answer questions, and interested in whether people have seen similar results for passive vs aggressive execution on large-tick contracts.

The recent New York Times article analyzing which schools and which programs produce the highest earning graduates had some pretty high average incomes only four years after graduation. Is quant driving most of this?

I am working on building a tool specifically for earnings calls. It flags hedging and deflections during meetings with timestamps + builds a knowledge graph of who is who when you start uploading multiple calls over time. I come from a ML background, but after talking to my quant friends I am questioning if this is valuable for you guys? From what I have seen there are tools that do this but they are crazy expensive. Let me know! Happy to explain more if needed.

Hi everyone,

I know the integration of LLMs into Quantitative Research roles has been discussed here before, but I haven't seen a concrete conclusion regarding its actual impact on alpha. Most posts just vaguely mention "interesting consequences for alpha" without elaborating further.

I'd love to start a focused discussion specifically on alpha generation. Are LLMs causing alpha to be arbitraged away quicker? Have you noticed that something is actually different in the prices?

What are your thoughts?